Cloud computing has transformed data management for businesses and individuals alike by making systems more scalable and economically viable. However, the distributed architecture in cloud computing inherently makes it vulnerable to highly sophisticated cyber threats, such as DDoS attacks, ransomware, cryptojacking, and many others that could compromise data confidentiality, integrity, and availability. Traditional intrusion detection systems face limitations such as high false-negative rates and low adaptability to emerging threats. This manuscript proposes a blockchain-integrated discrete Hopfield neural network and edge attention networks with duck swarm optimization (Hop-MEA-Duck) to enhance cloud privacy and develop a robust, privacy-preserving intrusion detection framework for cloud environments. The framework assumes a two-level privacy mechanism. The former provides privacy through the Adaptive Blockchain Sharding Protocol with Hybrid Consensus (Hyb-BCSP), which enhances data security and scalability. The second tier comprises preprocessing using the Adaptive Self-Guided Loop Filter (ASGLF) and feature selection via the Pufferfish Optimization Algorithm (POA). The Discrete Hopfield Neural Network (DHNN) with Multilayer Edge Attention Network (MEAN) is used to classify normal and abnormal behaviors, and the Duck Swarm Algorithm (DSA) is used at the expense of hyperparameter tuning. The results of the experiments indicate that the proposed framework is practical, achieving 97.8% detection accuracy, 96.5% precision, and a significant reduction in the false-negative rate to 2.4%. Also, the preprocessing phase increased the relevance of the data by 85%, whereas the rate of information immutability in the blockchain was 99.2%. To sum up, the suggested framework can provide a privacy-preserving, scalable, and adaptable solution for detecting and removing cyber threats in the context of cloud computing. It can fill significant gaps in current intrusion detection approaches.